The manufacturing sector is envisioned to be heavily influenced by artificial intelligence-based technologies with the extraordinary increases in computational power and data volumes1,2. A central challenge in manufac...
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This paper addresses the issue of synchronization of switched delayed neural networks with communication delays via event-triggered control. For synchronizing coupled switched neural networks, we propose a novel event...
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Event-based social networks (EBSNs) are the newly emerging social platforms for users to publish events online and attract others to attend events offline. The content information of events plays an important role in ...
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ISBN:
(纸本)9781538641293
Event-based social networks (EBSNs) are the newly emerging social platforms for users to publish events online and attract others to attend events offline. The content information of events plays an important role in event recommendation. However, the content-based approaches in existing event recommender systems cannot fully represent the preference of each user on events since most of them focus on exploiting the content information from events' perspective, and the bag-of-words model, commonly used by them, can only capture word frequency but ignore word orders and sentence structure. In this paper, we shift the focus from events' perspective to users' perspective, and propose a Deep User Modeling framework for Event Recommendation (DUMER) to characterize the preference of users by exploiting the contextual information of events that users have attended. Specifically, we utilize convolutional neural network (CNN) with word embedding to deeply capture the contextual information of a user's interested events and build up a user latent model for each user. We then incorporate the user latent model into probabilistic matrix factorization (PMF) model to enhance the recommendation accuracy. We conduct experiments on the real-world dataset crawled from a typical EBSN, ***, and the experimental results show that DUMER outperforms the compared benchmarks.
Non-small cell lung cancer (NSCLC) is a malignant tumor, and contains three major subtypes which are difficult to be distinguished at early stages of NSCLC. Many pathways work together to perform certain functions in ...
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ISBN:
(纸本)9781509016129
Non-small cell lung cancer (NSCLC) is a malignant tumor, and contains three major subtypes which are difficult to be distinguished at early stages of NSCLC. Many pathways work together to perform certain functions in cells. One might expect the high level of co-appearance or repression of pathways to distinguish different subtypes of NSCLC. However, it is difficult to detect coordinated regulations of pathways by existing methods. In our work, the coordinated regulations of pathways are detected using modified higher logic analysis of gene expression data. Specifically, we identify the genes whose regulation obeys a logic function by the modified higher logic analysis, which focuses on the relationships among the gene triplets that are not evident when genes are examined in a pairwise fashion. Then, the relationships among genes are mapped to pathways to predict the coordinated regulated relationships among pathways. By comparing coordinated regulations of pathways, we find that the regulation patterns of pathways which are associated with cell death are different in three subtypes of NSCLC. This method allows us to uncover co-appearance or repression of pathways in high level, and it has a potential to distinguish the subtypes for complex diseases.
In the past nearly two decades, DNA self-assembly technology as a promising technology, a body of lab.ratory work has been emerged in an endless stream. Single-stranded DNA tile (SST) assembly provides a simple, modul...
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Purpose. Since fractal image coding is time-consuming and is prone to causing "blocking artifact", the article aims to combine fractal image coding, wavelet transform and compressed sensing to put forward a ...
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Indoor localization is a popular topic because of the poor performance of GPS in the indoor environment. This paper has provided a new method to achieve the goal of indoor localization. Combining the usage of posture ...
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The difference of protein sequence or protein structure can be used for the construction of molecular evolutionary tree or phytogenetic tree with certain hierarchy and topology. The divergent points in the tree sugges...
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Neural degeneration in Alzheimer's disease (AD) leads to structural topology deformation that in turn changes brain functionality. The main aim of the present study is to find the brain's functional connectivi...
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Neural degeneration in Alzheimer's disease (AD) leads to structural topology deformation that in turn changes brain functionality. The main aim of the present study is to find the brain's functional connectivity network (FCN) correlates of Alzheimer's psychological test scores. To this end, the brain's FCN is extracted from the resting state functional magnetic resonance images (rs-fMRI) of healthy controls and patients with AD and represented as a graph. Then, network measures are calculated from the graphs. The correlations between the brain network measures and five AD psychological assessment test scores are evaluated. The results show positive correlation between the Mini-Mental State Examination (MMSE) and nodal strength in the left insula and negative correlation between the Clinical Dementia Rating (CDR) score and local efficiency of left olfactory cortex and also eigenvector centrality of left supramarginal cortex. The Neuropsychiatric Inventory Questionnaire (NPI-Q) also seems to be correlated with network measures of the left superior parietal gyrus. Moreover, notable decreased continuity are spotted in the limbic system. These measures can be used to provide an objective tool for AD diagnosis.
In this paper, we propose a sampling approach of reference points used for performance metrics of multi-objective evolutionary algorithms. Traditional reference point sampling methods, such as the Das and Dennis metho...
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ISBN:
(纸本)9781509006243
In this paper, we propose a sampling approach of reference points used for performance metrics of multi-objective evolutionary algorithms. Traditional reference point sampling methods, such as the Das and Dennis method, usually sample the reference points via a set of uniformly distributed weight vectors generated on an ideal hyper-plane in objective space, which however often ignore the geometric shape of a specific Pareto front. Therefore, we propose a novel reference point sampling approach by taking the specific shape of the Pareto optimal front to be tackled into account for measuring the performance of multi-objective evolutionary algorithms. The performance of the proposed reference point sampling method against the other two state-of-the-art sampling methods is tested on six test instances in various conditions, which clearly demonstrate the effectiveness and superiority of the proposed sampling method.
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